Device Operation Abnormal Alarm Audio Detection Dataset

In the industrial and safety protection sectors, real-time monitoring of equipment and fault pre-warning are essential to ensuring production and personal safety. However, traditional human-based monitoring technologies often suffer from untimely responses and high false alarm rates, particularly failing to fully exploit the information contained in audio signals. This dataset addresses these issues, based on high-quality equipment operation audio collection, constructing a detection system centered on abnormal alarm audio. The data was collected using high-precision microphones in real industrial environments, covering various equipment and operating states, ensuring data accuracy and consistency through multiple rounds of annotation and consistency checks. The annotation team is composed of experts in acoustics and electrical engineering, ensuring professionalism and reliability. In data preprocessing, techniques such as noise elimination, signal enhancement, and feature extraction are employed to ensure final data processing performance. Data is stored in WAV format, organized into structures categorized by equipment type and anomaly type, facilitating subsequent analysis and model training. The Device Operation Abnormal Alarm Audio Detection Dataset demonstrates significant advantages in data quality, technological innovation, and practical application. The dataset achieves an annotation accuracy of 99%, and innovative algorithms have been implemented to enhance and refine audio signals. Detection models trained with this dataset have shown in experimental verification a significant improvement of more than 15% in alarm system accuracy. Compared to other audio datasets, ours provides unique scarcity in the diversity of equipment and anomaly state coverage. Additionally, it supports multi-platform development and integration, featuring high scalability and versatility.